A sieve stochastic gradient descent estimator for online nonparametric regression in Sobolev ellipsoids

Publication date: 8 December 2022

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2104.00846

zbMATH Keywords

online learning nonparametric regression stochastic gradient descent

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08)

Related Items (2)

Regression in Tensor Product Spaces by the Method of Sieves ⋮ A sieve stochastic gradient descent estimator for online nonparametric regression in Sobolev ellipsoids

Uses Software

ElemStatLearn
gamair
GaussQR

Cites Work

Unnamed Item
Unnamed Item
Unnamed Item
Unnamed Item
Unnamed Item
Unnamed Item
Unnamed Item
Unnamed Item
Unnamed Item
Unnamed Item
Unnamed Item
Nonparametric stochastic approximation with large step-sizes
Statistical and computational trade-offs in estimation of sparse principal components
Mercer's theorem on general domains: on the interaction between measures, kernels, and RKHSs
Minimax optimal rates of estimation in high dimensional additive models
Mercer theorem for RKHS on noncompact sets
A reproducing kernel Hilbert space approach to functional linear regression
Minimax estimation in sparse canonical correlation analysis
Tractability of multivariate problems. Volume I: Linear information
Online gradient descent learning algorithms
Additive regression and other nonparametric models
Optimal rates of convergence for nonparametric estimators
Sparse CCA: adaptive estimation and computational barriers
A distribution-free theory of nonparametric regression
A sieve stochastic gradient descent estimator for online nonparametric regression in Sobolev ellipsoids
Just interpolate: kernel ``ridgeless regression can generalize
Computational barriers in minimax submatrix detection
Optimal rates for the regularized least-squares algorithm
Computational and statistical boundaries for submatrix localization in a large noisy matrix
On the mathematical foundations of learning
On Martingale Extensions of Vapnik–Chervonenkis Theory with Applications to Online Learning
Kernel-based Approximation Methods using MATLAB
Online Learning as Stochastic Approximation of Regularization Paths: Optimality and Almost-Sure Convergence
Curve fitting by polynomial-trigonometric regression
Large-Scale Machine Learning with Stochastic Gradient Descent
Support Vector Machines
High-Dimensional Statistics
Reconciling modern machine-learning practice and the classical bias–variance trade-off
Theory for penalised spline regression
Computational Complexity
An Online Projection Estimator for Nonparametric Regression in Reproducing Kernel Hilbert Spaces

This page was built for publication: A sieve stochastic gradient descent estimator for online nonparametric regression in Sobolev ellipsoids